
The first layer of the recurrent neural network (RNN)-based encoder network of Survival Seq2Seq is made of Gated Recurrent Units with Decay (GRU-D) [1]} cells. GRU-D cells offer superior performance in imputing not-missing-at-random values. Taking advantage of GRU-D, Survival Seq2Seq can effectively handle high missing rates that commonly occur among medical datasets.

The decoder network of our model is a recurrent network, which can generate substantially smoother PDFs compared to other non-parametric survival models. Since Survival Seq2Seq has fewer trainable parameters compared to a decoder made of dense layers, it suffers less from overfitting compared to other non-parametric models that use Multi-Layer Perceptron (MLP) in their decoders.

We have enhanced the typical loss function used for training non-parametric survival models by improving the ranking loss term of our model. The improved ranking loss will help the model to better rank samples with longer event times.

Our proposed model can be effectively trained on datasets with a long-tailed distribution, which is a common characteristic of healthcare datasets. This means that Survival Seq2Seq can accurately predict longer event times as well as the shorter ones.

